Channel estimation refers to a process of obtaining space channel information from a known transmitted pilot, a time-frequency location at which the pilot is transmitted, and a data signal received at the time-frequency location. Taking a Long Term Evolution (LTE) downlink system as an example, if a downlink pilot transmission sequence is represented as S, a received signal is represented as Y, and a space frequency channel is represented as H+n, where H represents an impulse response of a radio fading channel, and n represents a Gaussian white noise, then Y=(H+n)·S, so that frequency channels at resource locations where all the downlink pilots are located can be estimated as Ĥ=H+n=Y/S. After Ĥ is derived, channels of data resources Ĥ1 are derived according to correlation between the pilot resources and the data resources, through one-dimension cascaded filtering interpolation, two-dimension filtering interpolation, etc., as commonly applied at present.
One-dimension cascaded filtering interpolation refers to that filtering interpolation is performed on data in a specified region 1 firstly in one dimension (e.g., in one of the time domain and the frequency domain) using pilots in a specified region 2 to derive channel estimation on symbols where the pilots are located, and filter interpolation is performed then in the other dimension (in the other of the time domain and the frequency domain) to derive channel estimation values over all the resources. Taking cascaded filtering interpolation in the time and frequency dimensions as an example, particularly if a filtering matrix in frequency domain is represented as Pf, and a filtering matrix in time domain is represented as Pt, then firstly frequency domain interpolation and then time domain interpolation is performed for example, so Ĥ1=Pt·Pf·Ĥ.
Two-dimension filtering interpolation refers to that filter interpolation is performed on data in a specified region 1 concurrently in both the dimensions using pilots in a specified region 2 to derive channel estimation values over all the resources. Taking filtering interpolation in both time and frequency dimensions as an example, particularly if a two-dimension filtering matrix in the frequency and time domains is represented as Pft, then Ĥ1=Pft·Ĥ.
In one-dimension cascaded filtering interpolation, the information about only the one dimension is used each time interpolation is performed without making full use of the information about the pilots having high correlation around the interpolation points, thus resulting in relatively low precision of channel estimation. In two-dimension filtering interpolation, the matrix in use may be large, thus resulting in a significant calculation effort.